Logistics node tracing method and apparatus

ABSTRACT

The present disclosure provides a logistics node tracing method and apparatus for finding a trace node among logistics nodes in a logistics chain network corresponding to a logistics unit. The method includes: obtaining chain network information of a logistics chain network corresponding to a logistics unit, and determining a target analysis domain and confidence node(s) of the logistics unit according to the chain network information; determining fast node(s) according to the chain network information, the target analysis domain, and a timeliness level of each of the logistics nodes in the logistics chain network; determining a predicted logistics route corresponding to the logistics unit according to the chain network information, the target analysis domain, and the confidence node(s); and determining the trace node corresponding to the logistics unit according to the fast node(s) and the predicted logistics route.

CROSS REFERENCE TO RELATED APPLICATIONS

The present disclosure claims priority to Chinese Patent Application No.202010622825.2, filed Jul. 1, 2020, which is hereby incorporated byreference herein as if set forth in its entirety.

BACKGROUND 1. Technical Field

The present disclosure relates to logistics route prediction technology,and particularly to a logistics node tracing method and a logistics nodetracing apparatus.

2. Description of Related Art

The logistics chain network refers a directed acyclic graph formed bynodes for representing all the elements of a logistics system that arerelated to transactions or other product related works (e.g.,warehousing and transportation) and directed arrows each representingthe circulation relationship of a logistics unit (e.g., a logisticsitem, logistics truck, or logistics personnel) between two nodes. Theprocess of constructing the logistics chain network is the process ofconstructing the directed acyclic graph, that is, through a presentcirculation information data set of logistics units, a logistics chainnetwork is constructed using the nodes on the flow routes and the floworder between nodes of all the logistics units.

The tracing of logistics units can be divided into discrete batchlogistics unit tracing and continuous batch logistics unit tracing fromthe view of the flow method. The former is mainly to study the flowsequence of one or more batches of logistics units between the nodes inthe logistics chain network, while the latter is mainly to study theprocess of splitting and mixing the logistics units. As to the discretebatch logistics unit tracing, the tracing methods based on tracing marksare currently mostly used, which mainly include barcode technology,radio frequency identification technology, and biometric technology.

However, in the current researches on the tracing of logistics units, afew of them have considered about how to use the existing incompletedata to realize the tracing of logistics units in the case that thechain of tracing information is broken and the information isincomplete.

In the current applications of tracing logistics units in the absence oftracing data, a logistics unit often has the same or similar flow routewith other logistics units with smaller general dissimilarities. In theexiting methods for tracing logistics units using incomplete data chain,the distribution of the flow times of the logistics units between allthe node pairs with the connection relationship with respect to thelogistics chain network is used for modeling to obtain the flow timedistribution model for the nodes and perform predictions, therebycalculating the predicted flow time on routes. However, in the exitingmethods for tracing logistics units using incomplete data chain, thereare problems of excessively large analysis domain, small modelgranularity, low reliability of the flow routes of logistics units whichis incapable of meeting the analysis timeliness requirements of certainnodes, and the like.

SUMMARY

In view of solving the above-mentioned problems, the present disclosureprovides a logistics node tracing method and a logistics node tracingapparatus to overcome the problems or at least partially solve theproblems as follows.

A logistics node tracing method for finding at least a trace node amonglogistics nodes in a logistics chain network corresponding to alogistics unit is provided. In which, the logistics chain network iscomposed of a plurality of logistics routes, and each of the logisticsroutes is composed of a plurality of logistics nodes connected in asingle direction. The method includes steps of:

obtaining chain network information of the logistics chain networkcorresponding to the logistics unit, and determining a target analysisdomain and one or more confidence nodes of the logistics unit accordingto the chain network information, wherein the chain network informationcomprises logistics node information of each of the logistics nodes;

determining one or more fast nodes according to the chain networkinformation, the target analysis domain, and a timeliness level of eachof the logistics nodes in the logistics chain network;

determining a predicted logistics route corresponding to the logisticsunit according to the chain network information, the target analysisdomain, and the one or more confidence nodes; and

determining the trace node corresponding to the logistics unit accordingto the one or more fast nodes and the predicted logistics route.

In an example, the step of determining the one or more fast nodesaccording to the chain network information, the target analysis domainand the timeliness level of each of the logistics nodes in the logisticschain network can include:

determining a first sub-chain network according to the chain networkinformation and the target analysis domain; and

determining the one or more fast nodes according to the first sub-chainnetwork and the timeliness level of each of the logistics nodes in thelogistics chain network.

In an example, the step of determining the predicted logistics routecorresponding to the logistics unit according to the chain networkinformation, the target analysis domain, and the one or more confidencenodes can include:

determining a second sub-chain network according to the first sub-chainnetwork and the fast node; and

determining the predicted logistics route according to the secondsub-chain network and the one or more confidence nodes.

In an example, the step of determining the trace node corresponding tothe logistics unit according to the one or more fast nodes and thepredicted logistics route can include:

determining the logistics node located before the one or more fast nodesin the predicted logistics route as the trace node.

In an example, the step of determining the first sub-chain networkaccording to the chain network information and the target analysisdomain can include:

determining a node type of each of the logistics nodes in the logisticschain network according to the chain network information, where the nodetype includes a start node, an end node, a fork node, a forking startnode, and a midway node; and

generating the first sub-chain network according to the start node, theend node, the fork node, and the forking start node.

In an example, the step of determining the fast node according to thefirst sub-chain network and the timeliness level of each of thelogistics nodes in the logistics chain network can include:

determining the forking start node corresponding to the fork node withthe highest timeliness level according to the timeliness level of eachof the logistics nodes in the first sub-chain network, and setting theforking start node as a fast forking start node;

determining the fork node corresponding to the fast forking start nodeas the fast forking node, and generating a third sub-chain networkaccording to the start node, the end node, and the fast forking node;

determining an expected time to move the logistics unit from the startnode to the end node through each of the fast forking nodes in the thirdsub-chain network; and

setting the fast forking node corresponding to the minimum expected timeas the fast node.

In an example, the step of determining the second sub-chain networkaccording to the first sub-chain network and the fast node can include:

removing the fast forking start node and the fast forking node in thefirst sub-chain network; and

generating the second sub-chain network according to the remaininglogistics nodes in the first sub-chain network.

In an example, the step of determining the predicted logistics routeaccording to the second sub-chain network and the one or more confidencenodes can include:

determining the expected time to move the logistics unit from the startnode to the end node through each of the forking nodes in the secondsub-chain network; and

generating the predicted logistics route according to the expected timeand the one or more confidence nodes.

In an example, the step of generating the predicted logistics routeaccording to the expected time parameter and the one or more confidencenodes can include:

setting the logistics route with the largest number of confidence nodesas the predicted logistics route, in response to there being logisticsroutes with the same expected time; and

setting the logistics route with the minimum expected time as thepredicted logistics route, in response to there being no logistics routewith the same expected time.

Furthermore, a logistics node tracing apparatus for finding at least atrace node among logistics nodes in a logistics chain networkcorresponding to a logistics unit

In which, the logistics chain network is composed of a plurality oflogistics routes, and each of the logistics routes is composed of aplurality of logistics nodes connected in a single direction. Theapparatus includes: a memory, a processor, and a computer programsstored in the memory and executable on the processor, where the computerprogram include:

instructions for obtaining chain network information of the logisticschain network corresponding to the logistics unit, and determining atarget analysis domain and one or more confidence node of the logisticsunit according to the chain network information, wherein the chainnetwork information comprises logistics node information of each of thelogistics nodes;

instructions for determining one or more fast nodes according to thechain network information, the target analysis domain, and a timelinesslevel of each of the logistics nodes in the logistics chain network;

instructions for determining a predicted logistics route correspondingto the logistics unit according to the chain network information, thetarget analysis domain, and the one or more confidence nodes; and

instructions for determining the trace node corresponding to thelogistics unit according to the one or more fast nodes and the predictedlogistics route.

The embodiments of the present disclosure have the advantages asfollows.

In the embodiment of the logistics node tracing method/apparatus, itobtains chain network information of the logistics chain networkcorresponding to the logistics unit, and determines a target analysisdomain and one or more confidence nodes of the logistics unit accordingto the chain network information, where the chain network informationincludes logistics node information of each of the logistics nodes;determines one or more fast nodes according to the chain networkinformation, the target analysis domain, and a timeliness level of eachof the logistics nodes in the logistics chain network; determines apredicted logistics route corresponding to the logistics unit accordingto the chain network information, the target analysis domain, and theone or more confidence nodes; and determines the trace nodecorresponding to the logistics unit according to the one or more fastnodes and the predicted logistics route. According to the differenttimeliness requirements of the changeable nodes in the streamlinedlogistics chain network, the changeable nodes are divided into fastnodes and slow nodes, thereby constructing a fast and streamlinedlogistics chain network. In which, the route of the logistics unit isanalyzed via the fast nodes, and the fast nodes at which the logisticsunit passing through is determined first, then the complete flow routeof the logistics unit is further determined, thereby improving thetracing efficiency. The confidence nodes are used as the basis fordetermining the tracing of the predicted logistics route among multiplepossible flow routes for the logistics unit, thereby improving thetracing reliability on the logistics unit.

BRIEF DESCRIPTION OF THE DRAWINGS

To describe the technical schemes in the embodiments of the presentdisclosure or in the prior art more clearly, the following brieflyintroduces the drawings required for the descriptions in the presentdisclosure. It should be understood that, the drawings in the followingdescription merely show some embodiments of the present disclosure. Forthose skilled in the art, other drawings can be obtained according tothe drawings without creative efforts.

FIG. 1 is a flow chart of an embodiment of a logistics node tracingmethod according to the present disclosure.

FIG. 2 is a schematic view of a logistics chain network of the logisticsnode tracing method of the embodiment of FIG. 1.

FIG. 3 is a schematic view of a first sub-chain network of the logisticsnode tracing method of the embodiment of FIG. 1.

FIG. 4 is a schematic view of a second sub-chain network of thelogistics node tracing method of the embodiment of FIG. 1.

FIG. 5 is a schematic view of a third sub-chain network of the logisticsnode tracing method of the embodiment of FIG. 1.

FIG. 6 is a schematic block diagram of the structure of an embodiment ofa logistics node tracing apparatus according to the present disclosure.

FIG. 7 is a schematic block diagram of the structure of an embodiment ofa computing device according to the present disclosure.

DETAILED DESCRIPTION

In order to make the objects, features and advantages of the presentdisclosure more obvious and easy to understand, the technical solutionsof the present disclosure will be further described below with referenceto the drawings and the embodiments. Apparently, the describedembodiments are part of the embodiments of the present disclosure, notall of the embodiments. All other embodiments obtained by those skilledin the art based on the embodiments of the present disclosure withoutcreative efforts are within the scope of the present disclosure.

FIG. 1 is a flow chart of an embodiment of a logistics node tracingmethod according to the present disclosure. In this embodiment, alogistics node tracing method is provided. The method is for findingtrace node(s) among logistics nodes in a logistics chain networkcorresponding to a logistics unit. In which, the logistics chain networkis composed of a plurality of logistics routes, and each of thelogistics routes is composed of a plurality of the logistics nodesconnected in a single direction. The logistics unit is the objective tobe traced, which can be an object or a person that moves on thelogistics route, for example, a logistics item (e.g., a product orgoods), a logistics truck, or a logistics personnel, and the like. Themethod is a computer-implemented method executable for a processor. Inone embodiment, the method may be implemented through and applied to alogistics node tracing apparatus shown in FIG. 6 or implemented throughand applied to a computing device shown in FIG. 7.

As shown in FIG. 1, the method includes the following steps.

S110: obtaining chain network information of the logistics chain networkcorresponding to the logistics unit, and determining a target analysisdomain and confidence node(s) of the logistics unit according to thechain network information, where the chain network information includeslogistics node information of each of the logistics nodes;

S120: determining fast node(s) according to the chain networkinformation, the target analysis domain, and a timeliness level of eachof the logistics nodes in the logistics chain network;

S130: determining a predicted logistics route corresponding to thelogistics unit according to the chain network information, the targetanalysis domain, and the confidence node(s); and

S140: determining the trace node(s) corresponding to the logistics unitaccording to the fast node(s) and the predicted logistics route.

In this embodiments, it obtains chain network information of thelogistics chain network corresponding to the logistics unit, anddetermines a target analysis domain and confidence node(s) of thelogistics unit according to the chain network information, where thechain network information includes logistics node information of each ofthe logistics nodes; determines fast node(s) according to the chainnetwork information, the target analysis domain, and a timeliness levelof each of the logistics nodes in the logistics chain network;determines a predicted logistics route corresponding to the logisticsunit according to the chain network information, the target analysisdomain, and the confidence node(s); and determines the trace node(s)corresponding to the logistics unit according to the fast node(s) andthe predicted logistics route. According to the different timelinessrequirements of the changeable nodes in the streamlined logistics chainnetwork, the changeable nodes are divided into fast nodes and slownodes, thereby constructing a fast and streamlined logistics chainnetwork. In which, the route of the logistics unit is analyzed via thefast nodes, and the fast nodes at which the logistics unit passingthrough is determined first, then the complete flow route of thelogistics unit is further determined, thereby improving the tracingefficiency. The confidence nodes are used as the basis for determiningthe tracing of the predicted logistics route among multiple possibleflow routes for the logistics unit, thereby improving the tracingreliability on the logistics unit.

The logistics node tracing method of this exemplary embodiment will befurther explained as follows.

In step S110, the chain network information of the logistics chainnetwork corresponding to the logistics unit(s) is obtained, and thetarget analysis domain and the confidence node(s) of the logisticsunit(s) are determined according to the chain network information. Inwhich, the obtained chain network information includes the logisticsnode information of each of the logistics nodes and connectionrelationships between the logistics nodes. In this embodiment, thelogistics node information includes location information and a node typeof the logistics node, where the node type can include, for example, astart node, an end node, a fork node, a forking start node, and a midwaynode.

In this embodiment, the chain network information is obtained from thelogistics system. The chain network information is obtained in responseto, for example, a request for determining trace nodes among thelogistics nodes in the logistics chain network which is received fromthe logistics system. The logistics system includes a computer systemcoupled to the logistics chain network, where the logistics system canbe incorporated with the logistics chain network and coupled to thelogistics chain network through, for example, a system bus, or beindependent from the logistics chain network and coupled to thelogistics chain network through, for example, a network such as theInternet. In addition, the logistics system can further include sensors(e.g., radio frequency sensors, biometric sensors, and cameras) fordetecting logistics units which can be installed at, for example, eachlogistics node. The target analysis domain is a group in a clusteringresult of an incomplete data clustering method (e.g., “the missing dataimputation approach based on incomplete data clustering” (MIBOI)proposed by Wu, Sen et al.) performed on the logistics units (to betraced) to which the logistics units with missing tracing informationbelong to. It should be noted that, the target analysis domain is setdifferently according to different analysis situations. Taking thetracing of the logistics unit as an example, because there are nodeswith high timeliness requirements in the process of tracing thelogistics unit, the selection of fast nodes must be performed first. Thelogistics unit is traced to determine the nodes in the logistics chainnetwork through which it passes and the order of passing. Generally, thelarger the analysis domain, the longer the analysis time; otherwise, thesmaller the analysis domain, the shorter the analysis time. Therefore,in order to meet the timeliness requirements of the determination of thefast nodes, the target analysis domain of the logistics unit must bereduced. At the same time, in order to solve the problem of multiplepossible flow routes that may occur when further determining thecomplete flow route of the logistics unit after the fast nodes aredetermined, the confidence nodes need to be determined while generatingthe target analysis domain of the logistics unit. In which, each of theconfidence nodes is a logistics node among the logistics nodes in thelogistics chain network that has a confidence value of 1, where theconfidence value is a value filled to an attribute of the logistics unitwith missing tracing data which corresponds to each of the logisticsnodes by an incomplete data clustering based missing data imputationmethod (e.g., the MIBOI).

As an example, the process of determining the target analysis domain andthe confidence nodes of the logistics units (to be traced) isessentially a process of clustering incomplete data sets of thelogistics units and filling in missing values. Among the clusteringmethod for incomplete data, the method of the MIBOI proposed by Wu, Senet al. can be used. That is, the logistics nodes of the logistics chainnetwork node is introduced as a binary attribute of the logistics units,and the group to which the logistics units in the clustering result thathave missing tracing information belongs to is taken as the targetanalysis domain of the logistics units, the data filling results aretaken as confidence values of the nodes, and the node with theconfidence value of 1 is the confidence node.

In the specific clustering process, each of all the logistics units tobe traced is scanned at a time, starting from creating the first classfor the first scanned logistics unit, and the merging of the scannedlogistics unit with the class or the creation of a new class isperformed for each of the logistics units in one scan.

For the created class, only the constraint tolerance set is retained,rather than retaining the information of all the logistics units.Whether to create a new class depends on a pre-specified upper limit uof the dissimilarity for constraint tolerance set. For every logisticsunit scanned, find the class with the smallest dissimilarity forconstraint tolerance set after its merging, and determine whether thesmallest dissimilarity for constraint tolerance set is less than u. Ifso, it will be merged into the class; otherwise, a new class will becreated. After the above-mentioned clustering is completed, find theclass of the logistics unit with missing tracing data, and the class isthe target analysis domain of the logistics unit.

Based on the clustering result, for each constraint tolerance attribute,if its tolerance value is not “*”, the value of “*” in the attribute ofthe logistics unit in the class is replaced with the above-mentionedtolerance value. The filled value is the confidence value of the node,and the node with the confidence value of 1 is the confidence node.

In step S120, the fast node(s) are determined according to the chainnetwork information, the target analysis domain, and the timelinesslevel of each of the logistics nodes in the logistics chain network.

In one embodiment, step S120 can include the steps as follows.

Step S121 (not shown): determining a first sub-chain network accordingto the chain network information and the target analysis domain.

It should be noted that, the first sub-chain network is a chain networkobtained by streamlining the logistics chain network, that is, astreamlined chain network composed of streamlined route(s) re-formedafter removing midway nodes (i.e., the intermediate nodes in thelogistics route) of each logistics route in the logistics chain network.

Thus, the first sub-chain network is capable of improving the timelinessof tracing the trace nodes in the process of tracing the logistics unit.

In one embodiment, step S121 can include the steps as follows.

Step S1211 (not shown): determining a node type of each of the logisticsnodes in the logistics chain network according to the chain networkinformation, where the node type includes a start node, an end node, afork node, a forking start node, and a midway node.

Thus, by classifying each logistics node in the chain network accordingto the node types, non-important nodes can be efficiently filtered out,and the simpleness of the logistics chain network can be improved,thereby saving time for subsequent steps. In this embodiment, the nodetype of each of the logistics nodes is determined based on the node typein the logistics node information of the logistics node in the chainnetwork information.

Step S1212 (not shown): generating the first sub-chain network accordingto the start node, the end node, the fork node, and the forking startnode.

FIG. 2 is a schematic view of a logistics chain network of the logisticsnode tracing method of the embodiment of FIG. 1; and FIG. 3 is aschematic view of a first sub-chain network of the logistics nodetracing method of the embodiment of FIG. 1. As shown in FIG. 2-FIG. 3,as an example, after obtaining the target analysis domain of thelogistics unit, the original logistics chain network is streamlined.Assuming that the logistics chain network is as shown in FIG. 2, nodesN1-N11 represent the elements in the logistics chain network, and nodeNi and node Nj are connected by a directed arrow to indicate that inthis logistics chain network, there are relationships of logistics unitssuch as transactions and transportations between node Ni and node Nj.

According to the flow route data of the logistics units in the targetanalysis domain of the logistics unit, the streamlined first sub-chainnetwork can be obtained. In which, the analysis domain is a set oflogistics units to be traced that have smaller general dissimilarity.Therefore, the streamlined logistics chain network generally includesroutes with a few forks, for example, the chain network of FIG. 2 whichis composed of the dotted arrows and their related nodes, where nodesN2, N5, N6, N7, and N8 are changeable nodes, and N1, N4, N9, and N11 arefixed nodes. The same nodes in all the flow routes are deleted, and onlythe start node as well as the fork node of each route and its forkingstart node are retained to obtain the streamlined logistics chainnetwork, that is, the first sub-chain network as shown in FIG. 3.

Step S122 (not shown): determining the fast node(s) according to thefirst sub-chain network and the timeliness level of each of thelogistics nodes in the logistics chain network.

It should be noted that, the determination of certain nodes have hightimeliness requirements. For example, in the application of logisticsunit tracing, in the case that a problematic product flows into acertain area, it means that a certain logistics node with inspectionfunction has inspection flaws. Because of the urgency in identifying thelogistics nodes with inspection functions that have inspection flaws andblocking the inspection flaws, it is necessary to quickly determine thelogistics nodes with inspection functions at which the logistics unitsflow through, that is, priority must be given to the determination ofcertain logistics nodes with specific functions. At this time, thelogistics nodes with the specific functions are the fast nodes.

In one embodiment, Step S122 can include the steps as follows.

Step S1221 (not shown): determining the forking start node correspondingto the fork node with the highest timeliness level according to thetimeliness level of each of the logistics nodes in the first sub-chainnetwork, and setting the forking start node as a fast forking startnode.

Step S1222 (not shown): determining the fork node corresponding to thefast forking start node as the fast forking node, and generating a thirdsub-chain network according to the start node, the end node, and thefast forking node.

FIG. 4 is a schematic view of a second sub-chain network of thelogistics node tracing method of the embodiment of FIG. 1. Referring toFIG. 3 and FIG. 4, as an example, assuming that in the first sub-chainnetwork shown in FIG. 3, nodes N2 and N5 are the nodes with the highesttimeliness requirement, that is, the fast nodes. It is necessary toquickly determine whether node N2 or node N5 is passed through by thelogistics unit. Therefore, the first sub-chain network that has beenstreamlined once through the target analysis domain needs to be furtherstreamlined to determine the fast nodes first. After deleting all thenodes in the logistics chain network that are within the target analysisdomain except for the fast nodes, the starting node, and ending node,the further streamlined logistics chain network as shown in FIG. 4,namely the third sub-chain network can be obtained.

Thus, the scope of analysis can be minimized to quickly determine thefast nodes.

Step S1223 (not shown): determining an expected time to move thelogistics unit from the start node to the end node through each of thefast forking nodes in the third sub-chain network.

As an example, the flow time t of the logistics unit between two nodesin the third sub-chain network is taken as a random variable, n timesamples within the analysis domain are collected, and the sampleinterval is divided into k incompatible equidistant intervals, then thevalue of k can be determined by the empirical formula k=1.87(n−1)^(2/5)proposed by H. A. Sturges. In which, the sample interval refers to thedifference between the maximum value and the minimum value of the n timesamples collected. The number of samples within each interval iscounted, and the accumulative frequency of each interval is calculated,so as to initially predict the time distribution of the logistics units.

The maximum likelihood estimation is used to solve the time distributionparameter of the logistics unit. Taking the estimation of the flow timedistribution of the logistics unit between node N1 and node N5 in FIG. 2as an example, assuming that the random variable of the flow timebetween the two nodes is T, and the variable distribution in the initialestimation is a normal distribution, the maximum likelihood estimationcan be used to solve the normal distribution parameter. The probabilitydensity function is f(t, μ, σ), and the time sample values obtained aret₁, t₂, . . . , and t_(n), then the value of the join density functionis Π_(i=1) ^(n)f(t_(i), μ, σ) when the value of the random point (T₁,T₂, . . . T_(n)) is (t₁, t₂, . . . t_(n)). Therefore, according to themaximum likelihood estimation, the values of μ and a should be chosen tomaximize the probability. The likelihood function is as follows:

$\begin{matrix}{\begin{matrix}{{L\left( {\mu,\sigma^{2}} \right)} = {\prod\limits_{i = 1}^{n}{f\left( {t_{i},\mu,\sigma} \right)}}} \\{= {\prod\limits_{i = 1}^{n}{\frac{1}{\sqrt{2\pi}\sigma}e^{- \frac{\;_{{({t_{i} - \mu})}^{2}}}{2\sigma^{2}}}}}} \\{= \left( {2\pi\sigma^{2}} \right)^{{- \frac{n}{2}}e^{- \frac{\sum\limits_{i = 1}^{n}\;{({t_{i} - \mu})}^{2}}{2\sigma^{2}}}}}\end{matrix};} & (1)\end{matrix}$

in which, the likelihood function of formula (1) is:

$\begin{matrix}{{{l\left( {\mu,\sigma^{2}} \right)} = {{{- \frac{n}{2}}{\ln\left( {2\pi\sigma^{2}} \right)}} - {\frac{1}{2\sigma^{2}}{\sum\limits_{i = 1}^{n}\left( {t_{i} - \mu} \right)^{2}}}}};} & (2)\end{matrix}$

the partial derivatives of l(μ, σ2) with respect to μ and σ²,respectively, are calculated, and all of them are set to 0, then thefollowing likelihood equations will be obtained:

$\begin{matrix}{\left\{ \begin{matrix}{\frac{\partial{l\left( {\mu,\sigma^{2}} \right)}}{\partial\mu} = {{\frac{1}{\sigma^{2}}{\sum\limits_{i = 1}^{n}\left( {t_{i} - \mu} \right)^{2}}} = 0}} \\{\frac{\partial{l\left( {\mu,\sigma^{2}} \right)}}{\partial\sigma^{2}} = {{{- \frac{n}{2\sigma^{2}}} + {\frac{1}{2\sigma^{4}}{\sum\limits_{i = 1}^{n}\left( {t_{i} - \mu} \right)^{2}}}} = 0}}\end{matrix} \right.;} & (3)\end{matrix}$

by solving the likelihood equations, it obtains:

$\begin{matrix}{{{\overset{\hat{}}{\mu} = \overset{¯}{x}},{\overset{\hat{}}{\sigma} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\left( {x_{i} - \overset{¯}{x}} \right)^{2}}}}};} & (4)\end{matrix}$

the distribution parameters μ and σ are solved so as to determine thedistribution of the flow time of the logistics unit between node N1 andnode N5.

By using the above-mentioned method, the distribution of the flow timeof the logistics unit between node N1 and node N5, node N1 and node N2,node N5 and node N11, and node N2 and node N11 can be respectivelyobtained, and the expected flow time of the logistics unit between twonodes can be solved by:

$\begin{matrix}{{{\int_{- \infty}^{+ \infty}{\frac{x}{\sqrt{2\pi}\sigma}e^{- \frac{{({x - \mu})}^{2}}{2\sigma^{2}}}}}{= \mu}{= \overset{¯}{x}}}.} & (5)\end{matrix}$

Therefore, the expected time of each logistics route can be obtained,and the expected time of one logistics route is the sum of the expectedtimes of each route between nodes. For example, the expected route timeof the route N1→N5→N11 is E_(N) ₁ _(→N) ₅ +E_(N) ₅ _(→N) ₁₁ .

Step S1224 (not shown): setting the fast forking node corresponding tothe minimum expected time as the fast node.

Referring to 4, as an example, after obtaining the expected times of allthe logistics routes in the third sub-chain network through theforegoing steps, a reference route is selected according to theobjective of minimizing the preset time difference between the logisticsunit and the expected time of each logistics route, the fast forkingnode (N2 or N5) passed on the route is the fast node passed by thelogistics unit.

In step S130, the predicted logistics route corresponding to thelogistics unit is determined according to the chain network information,the target analysis domain, and the confidence node(s).

It should be noted that, after the fast nodes that the logistics unitpasses in the logistics chain network are obtained through the foregoingsteps, the complete route of the logistics route is further predicted.

The predicted logistics route can be used to further determine the nodewhere a hazard problem of the logistics unit is introduced, and be usedto trace the source of a safety problem of the logistics unit, and canalso be used to recommend a logistics route for the logistics unit to betransported.

In one embodiment, step S130 can include the steps as follows.

Step S131 (not shown): determining a second sub-chain network accordingto the first sub-chain network and the fast node(s).

In one embodiment, Step S131 can include the steps as follows.

Step S1311 (not shown): removing the fast forking start node and thefast forking node in the first sub-chain network.

Step S1312 (not shown): generating the second sub-chain networkaccording to the remaining logistics nodes in the first sub-chainnetwork.

FIG. 5 is a schematic view of a third sub-chain network of the logisticsnode tracing method of the embodiment of FIG. 1. As shown in FIG. 5, itshould be noted that since the fast nodes that the logistics unit passesthrough have been determined through the foregoing steps, the passedfast nodes in the logistics chain network that are within the analysisdomain can be taken as fixed nodes to be removed while other changeablenods are remained unchanged, and the second sub-chain network shown inFIG. 5 is obtained.

Step S132 (not shown): determining the predicted logistics routeaccording to the second sub-chain network and the confidence node(s).

In one embodiment, Step S132 can include the steps as follows.

Step S1321 (not shown): determining the expected time to move thelogistics unit from the start node to the end node through each of theforking nodes in the second sub-chain network.

It should be noted that, the calculation method of the expected timeperformed in this step is the same as the calculation method of theexpected time of the route N1→N5 in the forgoing step. For the specificprocess, refer to the description of the foregoing step, which will notbe repeated herein.

Step S1322 (not shown): generating the predicted logistics routeaccording to the expected time and the confidence node(s).

As a result, the effectiveness of the determined predicted logisticsroute can be improved, and the prediction efficiency can be improved.

In one embodiment, Step S1322 can include the steps as follows.

Step S13221 (not shown): setting the logistics route with the largestnumber of confidence nodes as the predicted logistics route, in responseto there being logistics routes with the same expected time; and

Step S13222 (not shown): setting the logistics route with the minimumexpected time as the predicted logistics route, in response to therebeing no logistics route with the same expected time.

Referring to FIG. 5, as an example, after calculating the flow timedistribution of the logistics unit between node N1 and node N8, node N1and node N6, node N1 and node N7, node N8 and node N11, node N6 and nodeN11, and node N7 and node N11, respectively, the expected times of threeroutes are obtained. Since there may be multiple possible routes in thedetermination of the route of the other changeable nodes except the fastnodes, a preset route selection threshold γ is set to take all theroutes with the expected time difference from the expected time of thereference route of less than γ as possible routes.

If a plurality of possible routes are solved, the confidence nodes areused as the basis for the prediction of the route of the logistics unit,and the route including more confidence nodes is regarded as the flowroute of the logistics unit. After determining the fast nodes of theflow of the logistics unit and other changeable nodes, by using the dataof the fixed nodes obtained by counting in the target analysis domain ofthe logistics unit, the complete predicted logistics route of thelogistics unit in the logistics chain network can be obtained.

In step S140, the trace node(s) corresponding to the logistics unit isdetermined according to the fast node(s) and the predicted logisticsroute.

In this embodiment, the trace node is provided to the logistics systemby, for example, transmitting a response for the request for determiningthe trace nodes among the logistics nodes in the logistics chain networkwhich includes the trace node to the logistics system. In oneembodiment, step S140 can include the steps as follows.

Step S141 (not shown): determining the logistics node located before thefast node(s) in the predicted logistics route as the trace node(s).

The logistics node before each fast node is determined as a trace node.The trace node is a (suggested) node for an investigator to investigatein the case that, for example, there is a logistics node with inspectionflaw in the logistics chain network. Thus, the number of the logisticsnodes for the investigator to investigate can be reduced, so as toimprove the efficiency and accuracy of the tracing of the logisticsunit.

In this embodiment, it addresses the problems of excessively largeanalysis domain, small model granularity, low reliability of the flowroutes of logistics units which is incapable of meeting the analysistimeliness requirements of certain nodes, and the like in theconventional methods for tracing logistics units using incomplete datachain by dividing the nodes in the chain network into changeable nodesand fixed nodes to analyze the changeable nodes. Furthermore, thechangeable nodes are divided into the fast nodes and the slow nodesaccording to the different analysis timeliness requirements of thechangeable nodes. It introduces the attributes of the nodes of thelogistics chain network into the data sets of the logistics units toregard as incomplete data sets. The problem of predicting the flow routeof logistics units is taken as the problem of filling missing data inthe incomplete data sets, and the incomplete data clustering method isintroduced, and then the clustering result is taken as the targetanalysis domain of the logistics unit while the result of filling themissing data is taken as the confidence value of the node, therebydetermining the confidence nodes. The streamlined logistics chainnetwork (i.e., the first sub-chain network) is determined through thetarget analysis domain of the logistics unit, and then the streamlinedlogistics chain network (i.e., the second sub-chain network) is furtherdetermined. On this basis, the logistics node tracing method usingincomplete data chain is used to quickly determine the fast nodesthrough which the logistics unit flows first, and further determine thechangeable node through which the logistics unit flows. If there are aplurality of possible routes, the confidence nodes are introduced todistinguish the flow route, thereby increasing the reliability of theprediction of the flow route of the logistics unit. The analysis domainis limited to the data set of the logistics unit that has smallergeneral dissimilarity to the logistics unit with missing tracing data,so as to narrow the analysis domain of the methods for tracing logisticsunits using incomplete data chain and exclude irrelevant nodes and data.The time distribution model of the logistics unit is solved based on thestreamlined logistics chain network, which increases the modelgranularity and reduces the complexity of the calculation process.

Referring to FIG. 2-FIG. 5, in this embodiment, in order to verify thefast nodes obtained using the logistics chain network that the logisticsunit flows through, and to further determine the effectiveness of thelogistics unit flow route, it takes the logistics chain network shown inFIG. 2 as an example to perform simulation and analysis. Assuming thatthe logistics chain network constructed based on the historical data setof logistics unit is as shown in FIG. 2, and it is known that a certainlogistics unit starts from the end node N1 and flows between thesubsequent nodes N2-N12, where the tracing data of the logistics unit islost and its flow route has to be determined.

The nodes in the logistics chain network are introduced as binaryattributes of the logistics unit. In the historical data set of alogistics unit, if the logistics unit passes through node N2, the valueof its binary attribute N2 is 1. As an example, the tracing data oflogistics unit A (not shown) is missing, that is, the values of theattributes of the attributes of the nodes N1-N12 are unknown. Theclustering method based on incomplete data is used to cluster thehistorical data set of the logistics unit. Assuming that there are 100logistics units of the class of including the logistics unit A afterclustering, and the filled values of the attribute of the nodes N1-N12are (1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0), then the flow data of the 100logistics units are analyzed to obtain the route involved in its flow isas shown by the dotted arrow and the corresponding node in FIG. 2, thatis, the first sub-chain network shown in FIG. 3.

Assuming that the nodes N2 and N5 are known as fast nodes, according tothe above-mentioned method, the nodes N4, N6, N7, and N8 and thecorresponding directed edges are deleted to obtain the second sub-chainnetwork as shown in FIG. 4.

In the circulation relationship of the logistics unit between the nodes,the flow time of the logistics unit between two nodes, for example, thedelivery time of the logistics unit between two nodes directly connectedwith a directed edge, can be approximated as floating around a certainvalue. That is, the flow time can be regarded as having a normaldistribution. If it is analyzed as having other distribution mannersbased on actual condition, it can also be predicted according to thefollowing steps.

Taking the prediction of the time distribution function f(t_(1, 2))between node N1 and node N2 as an example, the time distributioncharacteristic between nodes are solved so as to obtain the expectedtime of the route, thereby determining the fast nodes through which thelogistics unit flows. The flow time data (in the case that the number ofthe logistics units of the class including the logistics unit A is toolarge after clustering, an appropriate number of logistics units can beselected as random samples) of the obtained 100 logistics units iscollected to take as random samples t₁, t₂, t₃, . . . , and t₁₀₀, wherethe unit is h. The sample data is divided into 12 groups to divide theoverall value range into 12 mutually incompatible intervals, and asample frequency distribution table as shown in Table 1 is established.

TABLE 1 Group Values in Number of Accumulative Numbers Group FrequenciesFrequencies Frequencies 1 3.2215 1 0.01 0.01 2 3.3937 3 0.03 0.04 33.5124 6 0.06 0.10 4 3.6668 9 0.09 0.19 5 3.7817 14 0.14 0.33 6 3.900115 0.15 0.48 7 4.0223 18 0.18 0.66 8 4.1518 14 0.14 0.80 9 4.2624 8 0.080.88 10 4.3730 6 0.06 0.94 11 4.4908 3 0.03 0.97 12 4.5855 3 0.03 1.00

The frequency distribution table can be used to predict the distributionof variables. It is determined from Table 1 that the time distributionbetween nodes N1 and N2 obeys the normal distribution, and the expectedvalue is around 4. After calculation, the maximum likelihood predictedvalues of the normal distributed parameters μ and σ are μ=3.9702 andσ=0.3102, respectively. Therefore, the distribution of the flow time ofthe logistics unit between node N1 and node N2 is N(3.97, 0.10).Similarly, the distribution of the flow time of the logistics unitbetween each node is calculated as shown in Table 2.

TABLE 2 Departure Node Arrival Node Time Distribution N1 N5 N (3.23,0.07) N1 N2 N (3.97, 0.10) N5 N11 N (14.05, 0.06) N2 N11 N (15.88, 0.09)

The calculated expected flow times of the two routes in the secondsub-chain network are as shown in Table 3.

TABLE 3 Routes Expected Flow Times N1→N5→N11 17.28 N1→N2→N11 19.85

Assuming that the preset delivery time and receiving time of thelogistics unit with missing tracing data are known, the difference is19.50 h. The difference between the route N1→N2→N11 and the preset timeis 0.35 h, and the time difference between the route N1→N5→N11 and thepreset time is 2.22 h. According to the forgoing analysis, the fast nodepassed by the target analysis domain is N2.

After determining the fast nodes, it needs to further determine thecomplete flow route of the logistics unit. According to theabove-mentioned method, the distribution of the flow time of thelogistics units between the nodes connected with directed edges in thethird sub-chain network in FIG. 5 is as shown in Table 4.

TABLE 4 Departure Node Arrival Node Time Distribution N1 N8 N (9.52,0.09) N1 N7 N (10.43, 0.09) N1 N6 N (11.05, 0.08) N8 N11 N (6.24, 0.07)N7 N11 N (5.45, 0.11) N6 N11 N (6.88, 0.12)

Similarly, the calculated expected flow times of the three routes can beas shown in Table 5.

TABLE 5 Routes Expected Flow Times N1→N8→N11 15.78 N1→N7→N11 15.88N1→N6→N11 17.93

In the first sub-chain network, because there are generally morechangeable nodes, and the route branches generated by the changeablenodes are also more, so the difference between the expected flow time ofthe route and the preset time is directly used as the basis ofdetermination, which is easy to produce larger errors and leads to lowreliability of the prediction of the route of the logistics unit.Therefore, when determining the changeable nodes, a threshold γ is setin advance. In practical applications, the value of γ is set accordingto the order of magnitude of the flow time between two nodes, which isrecommended to set to 10%-20% of the average value of the flow time ofthe logistics unit between nodes. In the simulation analysis, theexpected flow time between two nodes is about 4 h, and the value of γcan be set to 0.5 h. The routes with the difference between the expectedthe flow time of the route and the preset time less than γ are allpossible routes. If the difference between the delivery time and thereceiving time of the logistics unit with missing tracing data is 16.2h, the routes N1→N8→N11 and N1→N7→N11 are all possible routes. If thereare a plurality of possible routes, the confidence nodes are used as thebasis for determining the route. The confidence values of the nodeattribute of the found logistics unit with missing tracing data is (1,0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0), it can be seen that the confidencevalue of node N7 is 1 and the confidence value of node N8 is 0, whichindicates that node N7 is a confidence node, and a route including moreconfidence nodes has a higher reliability. Therefore, the flow route ofthe logistics unit is N1→N7→N11.

After the forgoing analysis, the fast nodes and the changeable nodesthat the logistics units flow through in the streamlined logistics chainnetwork are respectively determined, and by using them together with thefixed node information, the complete flow route of the logistics unitcan be determined as: N1→N2→N4→N7→N9→N11.

In which, since node N2 is a fast node, the trace node can be node N1and node N2.

As for a device embodiment, since it is basically similar to the methodembodiment, the description as follows will be relatively simple. Forrelated parts, please refer to the description of the method embodiment.

FIG. 6 is a schematic block diagram of the structure of an embodiment ofa logistics node tracing apparatus according to the present disclosure.In this embodiment, a logistics node tracing apparatus (device) isprovided. The apparatus is for finding trace node(s) among logisticsnodes in a logistics chain network corresponding to a logistics unit. Inwhich, the logistics chain network is composed of a plurality oflogistics routes, and each of the logistics routes is composed of aplurality of the logistics nodes connected in a single direction. In oneembodiment, the apparatus may be implemented through and applied to acomputing device shown in FIG. 7 or be the computing device itself.

As shown in FIG. 6, the apparatus includes:

a first determination module 610 configured to obtain chain networkinformation of the logistics chain network corresponding to thelogistics unit, and determine a target analysis domain and confidencenode(s) of the logistics unit according to the chain networkinformation, where the chain network information includes logistics nodeinformation of each of the logistics nodes;

a second determination module 620 configured to determine fast node(s)according to the chain network information, the target analysis domain,and a timeliness level of each of the logistics nodes in the logisticschain network; and

a third determination module 630 configured to determine a predictedlogistics route corresponding to the logistics unit according to thechain network information, the target analysis domain, and theconfidence node(s).

a trace node determining module 640 configured to determine the tracenode(s) corresponding to the logistics unit according to the fastnode(s) and the predicted logistics route, and provide the trace node(s)to the logistics system.

In one embodiment, the second determination module 620 includes:

a first sub-chain network determining sub-module configured to determinea first sub-chain network according to the chain network information andthe target analysis domain; and

a fast node determining sub-module configured to determine the fastnode(s) according to the first sub-chain network and the timelinesslevel of each of the logistics nodes in the logistics chain network.

In one embodiment, the third determination module 630 includes:

a second sub-chain network determining sub-module configured todetermine a second sub-chain network according to the first sub-chainnetwork and the fast node(s); and

a predicted logistics route determining sub-module configured todetermine the predicted logistics route according to the secondsub-chain network and the confidence node(s).

In one embodiment, the trace node determining module 640 includes:

a trace node determining sub-module configured to determine thelogistics node located before the fast node(s) in the predictedlogistics route as the trace node.

In one embodiment, the first sub-chain network determining sub-moduleincludes:

a node type determining sub-module configured to determine a node typeof each of the logistics nodes in the logistics chain network accordingto the chain network information, where the node type includes a startnode, an end node, a fork node, a forking start node, and a midway node;and

a first sub-chain network generating sub-module configured to generatethe first sub-chain network according to the start node, the end node,the fork node, and the forking start node.

In one embodiment, the fast node determining submodule includes:

a fast forking start node determining submodule configured to determinethe forking start node corresponding to the fork node with the highesttimeliness level according to the timeliness level of each of thelogistics nodes in the first sub-chain network, and setting the forkingstart node as a fast forking start node;

a fast forking node determining submodule configured to determining thefork node corresponding to the fast forking start node as the fastforking node, and generating a third sub-chain network according to thestart node, the end node, and the fast forking node;

a first expected time determining sub-module configured to determine anexpected time to move the logistics unit from the start node to the endnode through each of the fast forking nodes in the third sub-chainnetwork; and

a fast node setting sub-module configured to set the fast forking nodecorresponding to the minimum expected time as the fast node.

In one embodiment, the second sub-chain network determining sub-moduleincludes:

a fast forking start node and fast forking node removal sub-moduleconfigured to remove the fast forking start node and the fast forkingnode in the first sub-chain network; and

a second sub-chain network generating sub-module configured to generatethe second sub-chain network according to the remaining logistics nodesin the first sub-chain network.

In one embodiment, the predicted logistics route determining sub-moduleincludes:

a second expected time determining sub-module configured to determinethe expected time to move the logistics unit from the start node to theend node through each of the forking nodes in the second sub-chainnetwork; and

a predicted logistics route generating sub-module configured to generatethe predicted logistics route according to the expected time and theconfidence node(s).

In one embodiment, the predicted logistics route generating sub-moduleincludes:

a first predicted logistics route setting sub-module configured to setthe logistics route with the largest number of confidence nodes as thepredicted logistics route, in response to there being logistics routeswith the same expected time; and

a second predicted logistics route setting sub-module configured to setthe logistics route with the minimum expected time as the predictedlogistics route, in response to there being no logistics route with thesame expected time.

In this embodiment, each of the above-mentioned modules/units isimplemented in the form of software, which can be computer program(s)stored in a memory of the logistics node tracing apparatus and includeinstructions executable on a processor of the logistics node tracingapparatus. In other embodiments, each of the above-mentionedmodules/units may be implemented in the form of hardware (e.g., acircuit of the logistics node tracing apparatus which is coupled to theprocessor of the logistics node tracing apparatus) or a combination ofhardware and software (e.g., a circuit with a single chipmicrocomputer).

FIG. 7 is a schematic block diagram of the structure of an embodiment ofa computing device according to the present disclosure. In thisembodiment, a computing device 12 is provided. The computing device 12is for predicting a logistics route for a logistics unit in a logisticschain network of a logistics system. In which, the logistics chainnetwork is composed of a plurality of logistics routes, and each of thelogistics routes is composed of a plurality of logistics nodes connectedin a single direction. The computing device 12 is coupled to thelogistics system through, for example, a system bus (e.g., an ISA bus)or a network (e.g., the Internet). In one embodiment, the computingdevice 12 may include the logistics node tracing apparatus shown in FIG.7 or be the logistics node tracing apparatus itself.

As shown in FIG. 7, the above-mentioned computing device 12 is in theform of a general-purpose computing device. The computing device 12 mayinclude, but are not limited to one or more processors or processingunits 16, a system storage 28, and a bus 18 connecting different systemcomponents (including the system storage 28 and the one or moreprocessing units 16).

The bus 18 may include a memory bus or a memory controller, a peripheralbus, a graphics acceleration port or processor, or a local bus using oneor more bus structures. The bus 18 may include one or more types of buswith different structures, for example, industry standard architecture(ISA) bus, microchannel architecture (MAC) bus, enhanced ISA bus, audioand video electronics standards association (VESA) local bus, andperipheral component interconnect (PCI) bus.

The computing device 12 typically includes a variety of computer systemreadable media. These media can be any media that can be accessed by thecomputing device 12, including volatile and non-volatile media as wellas removable and non-removable media.

The system storage 28 may include a computer system readable medium inthe form of volatile memory such as random access memory (RAM) 30 and/orcache memory 32. The computing device 12 may further include otherremovable/non-removable and volatile/nonvolatile computer system storagemedia. As an example, the storage system 34 may be used to read andwrite non-removable, non-volatile magnetic media (generally referred toas hard drive). Although not shown in FIG. 7, a disk drive for readingand writing removable non-volatile disks (e.g., floppy disks) and anoptical drive for reading and writing removable non-volatile opticaldisks (for example, CD-ROMs, DVD-ROMs, or other optical media) can beprovided. In these cases, each drive can be connected to the bus 18through one or more data medium interfaces. The system storage 28 mayinclude at least one program product, and the program product has a set(e.g., at least one) of program modules 42 configured to perform thefunctions of the embodiments of the present disclosure.

A program/utility tool 40 have a set (at least one) of program module 42which may be stored in, for example, a memory. The program module 42 caninclude, but is not limited to, an operating system, one or moreapplication programs, and other program modules and program data, andeach or some combinations of these examples may include theimplementation of a network environment. The program module 42 generallyexecutes the functions and/or methods in the embodiments described inthe present disclosure.

The computing device 12 may also communicate with one or more externaldevices 14 (e.g., keyboards, pointing devices, a display 24, andcameras), and may also communicate with one or more devices that enableusers to interact with the computing device 12, and/ or communicate withany device (e.g., a network card and a modem) that enables the computingdevice 12 to communicate with one or more other computing devices. Thiscommunication can be performed through an input/output (I/O) interface22. In addition, the computing device 12 may also communicate with oneor more networks (for example, a local area network (LAN)), a wide areanetwork (WAN), and/or a public network (e.g., the Internet) through anetwork adapter 20. As shown in FIG. 7, the network adapter 20communicates with other modules of the computing device 12 through thebus 18. It should be understood that, although not shown in FIG. 7,other hardware and/or software modules including, but not limited tomicrocode, a device driver, a redundant processing unit 16, an externaldisk drive array, a RAID system, a tape drive, and a data backup storagesystem 34 can be used in conjunction with the computing device 12.

The processing unit 16 executes the programs stored in the systemstorage 28 so as to execute various functional applications and dataprocessing such as implementing the above-mentioned logistics nodetracing method provided by the embodiments of the present disclosure.

That is, when the above-mentioned processing unit 16 executes theabove-mentioned program, it realizes: obtaining chain networkinformation of a logistics chain network corresponding to a logisticsunit, and determining a target analysis domain and confidence node(s) ofthe logistics unit according to the chain network information;determining fast node(s) according to the chain network information, thetarget analysis domain, and a timeliness level of each of the logisticsnodes in the logistics chain network; determining a predicted logisticsroute corresponding to the logistics unit according to the chain networkinformation, the target analysis domain, and the confidence node(s); anddetermining the trace node corresponding to the logistics unit accordingto the fast node(s) and the predicted logistics route.

In one embodiment, the present disclosure also provides acomputer-readable storage medium stored with computer program(s), andwhen the program(s) are executed by a processor, the above-mentionedlogistics node tracing method provided by the embodiments of the presentdisclosure is implemented.

That is, when the program is executed by the processor, it realizes:obtaining chain network information of a logistics chain networkcorresponding to a logistics unit, and determining a target analysisdomain and confidence node(s) of the logistics unit according to thechain network information; determining fast node(s) according to thechain network information, the target analysis domain, and a timelinesslevel of each of the logistics nodes in the logistics chain network;determining a predicted logistics route corresponding to the logisticsunit according to the chain network information, the target analysisdomain, and the confidence node(s); and determining the trace nodecorresponding to the logistics unit according to the fast node(s) andthe predicted logistics route.

Any combination of one or more computer-readable media may be used. Thecomputer-readable medium may be a computer-readable signal medium or acomputer-readable storage medium. The computer-readable storage mediummay be, but not limited to, an electrical, magnetic, optical,electromagnetic, infrared, or semiconductor system, device, orcomponent, or any combination of the above. As an example, thecomputer-readable storage media include: an electrical connection withone or more wires, a portable computer disk, a hard disk, a randomaccess memory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPOM), a flash, an optical fiber, a portable compactdisk read-only memory (CD-ROM), an optical storage device, a magneticstorage device, or any suitable combination of the above. In the presentdisclosure, the computer-readable storage medium can be any tangiblemedium that contains or stores programs, and the programs can be used byor in combination with an instruction execution system, device, orcomponent.

The computer-readable signal medium may include a data signal propagatedin a baseband or as a part of a carrier wave, where computer-readableprogram codes are carried therein. The propagated data signal can usemany forms including, but not limited to electromagnetic signals,optical signals, or any suitable combination of the foregoing. Thecomputer-readable signal medium may also be any computer-readable mediumother than the computer-readable storage medium. The computer-readablemedium may send, propagate or transmit the program for use by or incombination with an instruction execution system, apparatus, orcomponent.

The computer program codes for performing the operations of the presentdisclosure can be composed in one or more programming languages or acombination thereof. The above-mentioned programming languages mayinclude object-oriented programming languages such as Java, Smalltalk,C++, and also include conventional procedural programming language suchas C programming language or similar programming language. The programcode can be executed entirely on the computer of a user, partly on thecomputer of the user, executed as an independent software package,executed partly on the computer of the use and partly on a remotecomputer, or entirely executed on the remote computer or server. In thecase involving a remote computer, the remote computer can be connectedto the computer of the user through any kind of network including a LANor a WAN, or can be connected to an external computer (for example,connecting via the Internet provided by an Internet service provider).Each embodiment in the present disclosure is described in a progressivemanner, and each embodiment focuses on the differences from otherembodiments, hence the same or similar parts between the embodiments canbe referred to each other.

Although the preferred embodiments of the present disclosure have beendescribed, those skilled in the art can make additional changes andmodifications to these embodiments without creative efforts once theylearn of the basic creative concepts. Therefore, the appended claims areintended to be interpreted as including the preferred embodiments andall changes and modifications within the scope of the embodiments of thepresent disclosure.

Finally, it should be noted that in the present disclosure, therelational terms such as first and second are only used to distinguishone entity or operation from another entity or operation, and do notnecessarily require or imply that there is any such actual relationshipor order between these entities or operations. Moreover, the terms“include”, “comprise” or any other variants thereof are intended tocover non-exclusive inclusion, so that a process, method, object orterminal device including a series of elements not only includes thoseelements, but also includes other elements that are not explicitlylisted, or also include elements inherent to the process, method, objector terminal device. If there are no more restrictions, an elementdefined by the sentence “including a(n) . . . ” does not exclude theexistence of other same elements in the process, method, object orterminal device including the element.

The logistics node tracing method and the logistics node tracingapparatus provided by the present disclosure are described in detailabove. Embodiments are used in the present disclosure to illustrate theprinciple and implementation of the present disclosure. The descriptionsof the forgoing embodiment are only used to help understand thetechnical schemes of the present disclosure and their core ideas. At thesame time, for those skilled in the art, according to the ideas of thepresent disclosure, there will be changes in the specific implementationand the application scope. In summary, the contents of the presentdisclosure should not be construed as limitations to the presentdisclosure.

What is claimed is:
 1. A computer-implemented method for finding atleast a trace node among logistics nodes in a logistics chain networkcorresponding to a logistics unit; wherein the logistics chain networkis composed of a plurality of logistics routes, and each of thelogistics routes is composed of a plurality of logistics nodes connectedin a single direction; wherein the method comprises steps of: obtainingchain network information of the logistics chain network correspondingto the logistics unit from the logistics system, and determining atarget analysis domain and one or more confidence nodes of the logisticsunit according to the chain network information, wherein the chainnetwork information comprises logistics node information of each of thelogistics nodes; determining one or more fast nodes according to thechain network information, the target analysis domain, and a timelinesslevel of each of the logistics nodes in the logistics chain network;determining a predicted logistics route corresponding to the logisticsunit according to the chain network information, the target analysisdomain, and the one or more confidence nodes; and determining the tracenode corresponding to the logistics unit according to the one or morefast nodes and the predicted logistics route, and providing the tracenode to the logistics system.
 2. The method of claim 1, wherein the stepof determining the one or more fast nodes according to the chain networkinformation, the target analysis domain and the timeliness level of eachof the logistics nodes in the logistics chain network comprises:determining a first sub-chain network according to the chain networkinformation and the target analysis domain; and determining the one ormore fast nodes according to the first sub-chain network and thetimeliness level of each of the logistics nodes in the logistics chainnetwork.
 3. The method of claim 2, wherein the step of determining thepredicted logistics route corresponding to the logistics unit accordingto the chain network information, the target analysis domain, and theone or more confidence nodes comprises: determining a second sub-chainnetwork according to the first sub-chain network and the one or morefast nodes; and determining the predicted logistics route according tothe second sub-chain network and the confidence node.
 4. The method ofclaim 3, wherein the step of determining the trace node corresponding tothe logistics unit according to the one or more fast nodes and thepredicted logistics route comprises: determining the logistics nodelocated before the one or more fast nodes in the predicted logisticsroute as the trace node.
 5. The method of claim 2, wherein the step ofdetermining the first sub-chain network according to the chain networkinformation and the target analysis domain comprises: determining a nodetype of each of the logistics nodes in the logistics chain networkaccording to the chain network information, wherein the node typecomprises a start node, an end node, a fork node, a forking start node,and a midway node; and generating the first sub-chain network accordingto the start node, the end node, the fork node, and the forking startnode.
 6. The method of claim 5, wherein the step of determining the oneor more fast nodes according to the first sub-chain network and thetimeliness level of each of the logistics nodes in the logistics chainnetwork comprises: determining the forking start node corresponding tothe fork node with the highest timeliness level according to thetimeliness level of each of the logistics nodes in the first sub-chainnetwork, and setting the forking start node as a fast forking startnode; determining the fork node corresponding to the fast forking startnode as the fast forking node, and generating a third sub-chain networkaccording to the start node, the end node, and the fast forking node;determining an expected time to move the logistics unit from the startnode to the end node through each of the fast forking nodes in the thirdsub-chain network; and setting the fast forking node corresponding tothe minimum expected time as the one or more fast nodes.
 7. The methodof claim 6, wherein the step of determining the second sub-chain networkaccording to the first sub-chain network and the one or more fast nodescomprises: removing the fast forking start node and the fast forkingnode in the first sub-chain network; and generating the second sub-chainnetwork according to the remaining logistics nodes in the firstsub-chain network.
 8. The method of claim 7, wherein the step ofdetermining the predicted logistics route according to the secondsub-chain network and the one or more confidence nodes comprises:determining the expected time to move the logistics unit from the startnode to the end node through each of the forking nodes in the secondsub-chain network; and generating the predicted logistics routeaccording to the expected time and the one or more confidence nodes. 9.The method of claim 8, wherein the step of generating the predictedlogistics route according to the expected time parameter and the one ormore confidence nodes comprises: setting the logistics route with thelargest number of confidence nodes as the predicted logistics route, inresponse to there being logistics routes with the same expected time;and setting the logistics route with the minimum expected time as thepredicted logistics route, in response to there being no logistics routewith the same expected time.
 10. An apparatus for finding at least atrace node among logistics nodes in a logistics chain networkcorresponding to a logistics unit; wherein the logistics chain networkis composed of a plurality of logistics routes, and each of thelogistics routes is composed of a plurality of logistics nodes connectedin a single direction; wherein the apparatus comprises: a memory; aprocessor; and one or more computer programs stored in the memory andexecutable on the processor, wherein the one or more computer programscomprise: instructions for obtaining chain network information of thelogistics chain network corresponding to the logistics unit from thelogistics system, and determining a target analysis domain and one ormore confidence node of the logistics unit according to the chainnetwork information, wherein the chain network information compriseslogistics node information of each of the logistics nodes; instructionsfor determining one or more fast nodes according to the chain networkinformation, the target analysis domain, and a timeliness level of eachof the logistics nodes in the logistics chain network; instructions fordetermining a predicted logistics route corresponding to the logisticsunit according to the chain network information, the target analysisdomain, and the one or more confidence nodes; and instructions fordetermining the trace node corresponding to the logistics unit accordingto the one or more fast nodes and the predicted logistics route, andproviding the trace node to the logistics system.
 11. The apparatus ofclaim 10, wherein the instructions for determining the one or more fastnodes according to the chain network information, the target analysisdomain and the timeliness level of each of the logistics nodes in thelogistics chain network comprise: instructions for determining a firstsub-chain network according to the chain network information and thetarget analysis domain; and instructions for determining the one or morefast nodes according to the first sub-chain network and the timelinesslevel of each of the logistics nodes in the logistics chain network. 12.The apparatus of claim 11, wherein the instructions for determining thepredicted logistics route corresponding to the logistics unit accordingto the chain network information, the target analysis domain, and theone or more confidence nodes comprise: instructions for determining asecond sub-chain network according to the first sub-chain network andthe one or more fast nodes; and instructions for determining thepredicted logistics route according to the second sub-chain network andthe confidence node.
 13. The apparatus of claim 12, wherein theinstructions for determining the trace node corresponding to thelogistics unit according to the one or more fast nodes and the predictedlogistics route comprise: instructions for determining the logisticsnode located before the one or more fast nodes in the predictedlogistics route as the trace node.
 14. The apparatus of claim 11,wherein the instructions for determining the first sub-chain networkaccording to the chain network information and the target analysisdomain comprise: instructions for determining a node type of each of thelogistics nodes in the logistics chain network according to the chainnetwork information, wherein the node type comprises a start node, anend node, a fork node, a forking start node, and a midway node; andinstructions for generating the first sub-chain network according to thestart node, the end node, the fork node, and the forking start node. 15.The apparatus of claim 14, wherein the instructions for determining theone or more fast nodes according to the first sub-chain network and thetimeliness level of each of the logistics nodes in the logistics chainnetwork comprise: instructions for determining the forking start nodecorresponding to the fork node with the highest timeliness levelaccording to the timeliness level of each of the logistics nodes in thefirst sub-chain network, and setting the forking start node as a fastforking start node; instructions for determining the fork nodecorresponding to the fast forking start node as the fast forking node,and generating a third sub-chain network according to the start node,the end node, and the fast forking node; instructions for determining anexpected time to move the logistics unit from the start node to the endnode through each of the fast forking nodes in the third sub-chainnetwork; and instructions for setting the fast forking nodecorresponding to the minimum expected time as the fast node.
 16. Theapparatus of claim 15, wherein the instructions for determining thesecond sub-chain network according to the first sub-chain network andthe one or more fast nodes comprise: instructions for removing the fastforking start node and the fast forking node in the first sub-chainnetwork; and instructions for generating the second sub-chain networkaccording to the remaining logistics nodes in the first sub-chainnetwork.
 17. The apparatus of claim 16, wherein the instructions fordetermining the predicted logistics route according to the secondsub-chain network and the one or more confidence nodes comprise:instructions for determining the expected time to move the logisticsunit from the start node to the end node through each of the forkingnodes in the second sub-chain network; and instructions for generatingthe predicted logistics route according to the expected time and the oneor more confidence nodes.
 18. The apparatus of claim 17, wherein theinstructions for generating the predicted logistics route according tothe expected time parameter and the one or more confidence nodescomprise: instructions for setting the logistics route with the largestnumber of confidence nodes as the predicted logistics route, in responseto there being logistics routes with the same expected time; andinstructions for setting the logistics route with the minimum expectedtime as the predicted logistics route, in response to there being nologistics route with the same expected time.